From what I understand the pipeline depends strongly on homology to existing proteins that have determined structures to use substitution correlations to create an interaction graph which it then allows to evolve via learned rules.
I strongly suspect that as such it will not be very good at orphans without significant homology, be it sequence to structure or the reverse.
The naive way can be done immediately, AlphaFold2 is differentiable end-to-end, so you could immediately use gradient descent to optimise over the space of amino acid sequences (this is a discrete space, but you can treat it as continuous and add some penalties to your loss function to bias it towards the discrete points):
Correct sequence = argmin ( || AlphaFold2(x) - Target structure||)
For some notion of distance between structures.
I think that one problem is that an AA sequence generally results in a single, predictable 3D structure (at stable pH, and barring any misfolding events), whereas there are a lot of AA sequences that would result in something resembling e.g. an axle of a certain size, and even more that do not. It seems to me that this problem is in a different class of computational complexity.
AlphaFold 2 takes an amino acid sequence as input, and outputs a 3D structure which represents the protein that that sequence forms. It would be cool if we could do it in reverse, i.e. the user inputs a 3D model (e.g. a gear, an axle, a wall with a hole in it of a certain shape...) and then the system outputs an amino acid sequence that would form a protein with that structure.
I don't have a good sense of whether this is a very difficult problem that we are nowhere near solving, or an obvious next step after AlphaFold2.
My current median is that it's 4 years away, but I'm very uncertain about that.